Objective To compare the mid- and long-term clinical results of mitral valve plasty (MVP) and mitral valve replacement (MVR) in the treatment of functional mitral regurgitation (FMR). Methods Patients with FMR who underwent surgical treatment in the Department of Cardiovascular Surgery of the General Hospital of Northern Theater Command from 2012 to 2021 were collected. The patients who underwent MVP were divided into a MVP group, and those who underwent MVR into a MVR group. The clinical data and mid-term follow-up efficacy of two groups were compared. Results Finally 236 patients were included. There were 100 patients in the MVP group, including 53 males and 47 females, with an average age of (61.80±8.03) years. There were 136 patients in the MVR group, including 72 males and 64 females, with an average age of (61.29±8.97) years. There was no statistical difference in baseline data between the two groups (P＞0.05). There was no statistical difference between the two groups in the extracorporeal circulation time, aortic occlusion time, postoperative hospital and ICU stay, intraoperative blood loss, or hospitalization death (P＞0.05), but the time of mechanical ventilation in the MVP group was significantly shorter than that in the MVR group (P=0.022). The total follow-up rate was 100.0%, the longest follow-up was 10 years, and the average follow-up time was (3.60±2.55) years. There were statistical differences in the left atrial diameter, left ventricular end-diastolic diameter, left ventricular end-systolic diameter and cardiac function between the two groups compared with those before surgery (P<0.05). The postoperative left ventricular ejection fraction in the MVP group was statistically higher than that before surgery (P=0.002), but there was no statistical difference in the MVR group before and after surgery (P=0.658). The left atrial diameter in the MVP group was reduced compared with the MVR group (P=0.026). The recurrence rate of mitral regurgitation in the MVP group was higher than that in the MVR group, and the difference was statistically significant (10.0% vs. 1.5%, P=0.003). There were 14 deaths in the MVP group and 19 in the MVR group. The cumulative survival rate (P=0.605) and cardiovascular events-free survival rate (P=0.875) were not statistically significant between the two groups by Kaplan-Meier survival analysis. Conclusion The safety, and mid- and long-term clinical efficacy of MVP in the treatment of FMR patients are better than MVR, and the left atrial and left ventricular diameters are statistically reduced, and cardiac function is statistically improved. However, the surgeon needs to be well aware of the indications for the MVP procedure to reduce the rate of mitral regurgitation recurrence.

This paper summarizes Professor HAN Mingxiang's clinical experience in treating chronic obstructive pulmonary disease （COPD）. He believes that the key pathomechanism of COPD in the acute exacerbation stage is the invasion of external pathogens triggering latent illness， while lung qi deficiency is the primary mechanism in the stable stage. The core pathological factors throughout disease progression are deficiency， phlegm， and blood stasis. Treatment emphasizes a staged and syndrome-based approach. During the acute exacerbation stage， for wind-cold invading the lung syndrome， the self-formulated Sanzi Wenfei Decoction （三子温肺汤） is used to relieve the exterior， dispel cold， warm the lung， and resolve phlegm. For phlegm-dampness obstructing the lung syndrome， Huatan Jiangqi Fomulation （化痰降气方） is prescribed to warm the lung， transform phlegm， descend qi， and calm wheezing. For phlegm-heat obstructing the lung syndrome， Qingfei Huatan Fomulation （清肺化痰方） is applied to clear heat， resolve phlegm， moisten the lung， and stop coughing. For phlegm and blood stasis interlocking syndrome， Qibai Pingfei Fomulation （芪白平肺方） is used to tonify qi， resolve phlegm， and activate blood circulation to remove stasis. During the stable stage， for lung qi deficiency syndrome， Shenqi Wenfei Decoction （参芪温肺汤） is employed to warm the lung， tonify qi， resolve phlegm， and eliminate turbidity. For lung-spleen qi deficiency syndrome， Shenqi Buzhong Decoction （参芪补中汤） is utilized to strengthen the spleen， tonify qi， and reinforce metal （lung） from earth （spleen）. For lung-kidney deficiency syndrome， Shenqi Tiaoshen Fomulation （参芪调肾方） is prescribed to tonify the lung， warm yang， and regulate kidney function to calm wheezing. These strategies provide insights into the traditional Chinese medicine treatment of COPD.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Umbilical cord mesenchymal stem cells (UC-MSCs) have been widely used in regenerative medicine, but there is limited research on the stability of UC-MSCs formulation during production. This study aims to assess the stability of the cell stock solution and intermediate product throughout the production process, as well as the final product following reconstitution, in order to offer guidance for the manufacturing process and serve as a reference for formulation reconstitution methods. Three batches of cell formulation were produced and stored under low temperature (2-8 ℃) and room temperature (20-26 ℃) during cell stock solution and intermediate product stages. The storage time intervals for cell stock solution were 0, 2, 4, and 6 h, while for intermediate products, the intervals were 0, 1, 2, and 3 h. The evaluation items included visual inspection, viable cell concentration, cell viability, cell surface markers, lymphocyte proliferation inhibition rate, and sterility. Additionally, dilution and culture stability studies were performed after reconstitution of the cell product. The reconstitution diluents included 0.9% sodium chloride injection, 0.9% sodium chloride injection + 1% human serum albumin, and 0.9% sodium chloride injection + 2% human serum albumin, with dilution ratios of 10-fold and 40-fold. The storage time intervals after dilution were 0, 1, 2, 3, and 4 h. The reconstitution culture media included DMEM medium, DMEM + 2% platelet lysate, 0.9% sodium chloride injection, and 0.9% sodium chloride injection + 1% human serum albumin, and the culture duration was 24 h. The evaluation items were viable cell concentration and cell viability. The results showed that the cell stock solution remained stable for up to 6 h under both low temperature (2-8 ℃) and room temperature (20-26 ℃) conditions, while the intermediate product remained stable for up to 3 h under the same conditions. After formulation reconstitution, using sodium chloride injection diluted with 1% or 2% human serum albumin maintained a viability of over 80% within 4 h. It was observed that different dilution factors had an impact on cell viability. After formulation reconstitution, cultivation in medium with 2% platelet lysate resulted in a cell viability of over 80% after 24 h. In conclusion, the stability of cell stock solution within 6 h and intermediate product within 3 h meets the requirements. The addition of 1% or 2% human serum albumin in the reconstitution diluent can better protect the post-reconstitution cell viability.

It has become an industry consensus that self-assembled nanoparticles (SAN) are formed by molecular recognition of chemical components in traditional Chinese medicine during the decoction process. The insoluble components in the decoction are mostly in the form of nanoparticles, which can improve the problem of poor water solubility. However, the transfer rate of these insoluble components in the decoction is still very low, which limits the efficacy of the drug. This study aimed to refine the traditional decoction self-assembly phenomenon. The self-assembled nanoparticles were constructed by micro-precipitation method (MP-SAN), and characterized by particle size, zeta potential, stability index and morphology. The formation of MP-SAN and alterations in related physicochemical properties were evaluated using modern spectroscopic and thermal analysis techniques. The quality value transmitting pattern of lignan components within the MP-SAN was assessed via high performance liquid chromatography (HPLC). The MP-SAN showed sphere-like structure with uniform morphology, particle size of (245.3 ± 3.2) nm, polydispersity index (PDI) of (0.13 ± 0.03), zeta potential of (-48.9 ± 5.9) mV and stability index (SI) of (86.05% ± 2.27%). Comprehensive analyses using ultraviolet visible spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and other techniques confirmed molecular recognition between the decoction and ethanol extraction, leading to electron rearrangement under the influence of non-covalent bonding. This resulted in the formation of nanoparticles possessing superior thermal stability. As determined by HPLC, the encapsulation rates of the index components in the MP-SAN were all greater than 75% (dehydrodiconiferyl alcohol: 77.00%; herpetolide A: 78.57%; herpetrione: 94.53%), and the transfer rates were all higher than 65% (dehydrodiconiferyl alcohol: 96.01%; herpetolide A: 67.86%; herpetrione: 65.55%), which were 1.34, 1.38 and 4.81 times compared with those of the traditional decoction. In summary, this study successfully constructed the MP-SAN based on micro-precipitation method to achieve high transfer rate and high encapsulation rate of insoluble components in docoction, which provides a pharmaceutics idea for the efficient utilization of pharmacodynamic substance basis of traditional Chinese medicine.